The benefits of superior soft tissue contrast and versatility of MR imaging in providing anatomic, metabolic and physiological information has made MRI an important medical imaging tool for a variety of conditions, including the diagnosis of cancer and other soft tissue-related disorders. However, because MR imaging measures parameters such as proton density and MR relaxation rates, bone appears invisible in conventional MR images due to the low spin density and rapid relaxation associated with bone. For the visualization of bone tissue, additional medical imaging methods, such as CT imaging, are typically used.
Registration of the MR and CT images is typically used to produce a combined MR/CT image that includes both bone and soft tissues, but the process of registration is challenging because there are relatively view anatomical features visible in both MR images and CT images to serve as reference positions. As described above, MR images typically feature the distribution of various soft tissues but do not include bone, whereas CT images typically feature the distribution of bone but do not include various soft tissues.
At least several methods have been proposed to produce pseudo-CT images, defined herein as images that include a map of Hounsfield Units (HU) estimated from measured MR parameters obtained by an MR scanner. The pseudo-CT images include representations of soft tissues and bones derived from the same set of measured MR parameters, and therefore ameliorate any artifacts introduced by registration of multiple images obtained MR using separate devices. Further, the pseudo-CT images provide high-quality images of bones and attached soft tissues such as tendons, ligaments, and muscles without exposing the patient to potentially harmful radiation.
Atlas-based approaches typically rely on precompiled atlases of paired MR and CT images in combination with an algorithm to generate pseudo-CT images from the patient MR images. However, the atlas-based approaches are prone to errors for the imaging of patients falling outside of the anatomy represented by the population data used to compile the atlases. Direct MR imaging methods typically make use of MR signals obtained using specialized MRI data acquisition sequences, such as Dixon MRI sequences, ultra-short echo time (UTE) MRI sequences, or zero echo time (ZTE) MRI sequences to segment the resulting MR images into several tissue classes, but the challenge remains to determine the distribution of tissue densities within each segmented region based on MR signals alone. Additional direct MR imaging methods similarly perform UTE or ZTE MR imaging for tissue segmentation and further make use of a pre-calibrated conversion of an MR-derived quantity, such as DUTE R2* or the inverse logarithm of the ZTE signal, to determine a CT HU for the bone tissue. These additional direct MR imaging methods typically use MR-to-CT conversion relationships derived from population data, resulting in enhanced speed over previous atlas-based methods and better accounting for variations between individual subjects. However, the MR-to-CT conversion relationships used in these additional direct MR imaging methods potentially vary due to variations in measured MR signals and the MRI parameters derived from the measured MR parameters. For example, a DUTE R2* computation potentially varies depending on the TEs employed to obtain the MR imaging data, such as the water-fat in-phase or out-of-phase TEs used to obtain the second echo of a the DUTE sequence, and consequently potentially yields different R2* signals for different MRI scanners.
Images obtained using these additional direct MR imaging methods are also vulnerable to misclassification between bone/air and air/CSF interfaces due to poor imaging contrasts among these different tissue types. For example, the segmentation of tissues based on R2* images often results in major misclassifications in the sinus regions due to susceptibility effects near air-tissue interfaces. Further, bone and adipose tissue cannot be effectively separated using R2* signals or inverse logarithm of ZTE signals.
Further, these additional direct MR imaging methods assign constant values of parameters representative of tissue properties, such as linear attenuation coefficients (LACs) used in PET imaging data analysis, and thus fail to capture the heterogeneity of bone, soft and adipose tissue attenuation properties. For example, within the brain, the differences between LAC values of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) have been assumed to be low and therefore not likely to affect attenuation correction accuracy. However, since photon attenuation is a function of both LAC values and the thickness of tissue, the sheer amount of tissue present could introduce errors into the PET reconstruction if a homogeneous LAC distribution is assumed.